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Integrating R with C++: Rcpp, RInside and RProtoBuf

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Integrating R with C++: Rcpp, RInside and RProtoBuf

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Integrating R with C++: Rcpp, RInside and RProtoBuf Integrating R with C++: Rcpp, RInside and RProtoBuf Presentation Transcript

  • Integrating R with C++: Rcpp, RInside and RProtoBuf Dirk Eddelbuettel Romain François edd@debian.org romain@r-enthusiasts.com 22 Oct 2010 @ Google, Mountain View, CA, USA
  • R API inline Sugar R* Objects Modules End Preliminaries We assume a recent version of R such that install.packages(c("Rcpp","RInside","inline")) gets us current versions of the packages. RProtoBuf need the Protocol Buffer library and headers (which is not currently available on Windows / MinGW). All examples shown should work ’as is’ on Unix-alike OSs; most will also work on Windows provided a complete R development environment The Reference Classes examples assume R 2.12.0 and Rcpp 0.8.7. We may imply a using namespace Rcpp; in some of the C++ examples. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples A Simple Example Courtesy of Greg Snow via r-help density.default(x = xx) 0.5 0.4 > xx <- faithful$eruptions > fit <- density(xx) 0.3 Density > plot(fit) 0.2 0.1 0.0 1 2 3 4 5 6 N = 272 Bandwidth = 0.3348 Standard R use: load some data, estimate a density, plot it. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples A Simple Example Now complete density.default(x = xx) > xx <- faithful$eruptions > fit1 <- density(xx) 0.5 > fit2 <- replicate(10000, { + x <- sample(xx, replace=TRUE); + density(x, from=min(fit1$x), 0.4 + to=max(fit1$x))$y + }) > fit3 <- apply(fit2, 1, 0.3 Density + quantile,c(0.025,0.975)) > plot(fit1, ylim=range(fit3)) 0.2 > polygon(c(fit1$x, rev(fit1$x)), + c(fit3[1,], rev(fit3[2,])), + col='grey', border=F) 0.1 > lines(fit1) 0.0 1 2 3 4 5 6 N = 272 Bandwidth = 0.3348 What other language can do that in seven statements? Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples Motivation Chambers (2008) opens chapter 11 (Interfaces I: Using C and Fortran) with these words: Since the core of R is in fact a program written in the C language, it’s not surprising that the most direct interface to non-R software is for code written in C, or directly callable from C. All the same, including additional C code is a serious step, with some added dangers and often a substantial Chambers. Software for Data Analysis: amount of programming and debugging Programming with R. Springer, 2008 required. You should have a good reason. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples Motivation Chambers (2008) opens chapter 11 (Interfaces I: Using C and Fortran) with these words: Since the core of R is in fact a program written in the C language, it’s not surprising that the most direct interface to non-R software is for code written in C, or directly callable from C. All the same, including additional C code is a serious step, with some added dangers and often a substantial Chambers. Software for Data Analysis: amount of programming and debugging Programming with R. Springer, 2008 required. You should have a good reason. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples Motivation Chambers (2008) then proceeds with this rough map of the road ahead: Against: In Favor: It’s more work New and trusted Bugs will bite computations Potential platform Speed dependency Object references Less readable software So is the deck stacked against us? Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • Le viaduc de Millau
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples Rcpp in a Nutshell The goal: Seamless R and C++ Integration R offers the .Call() interface operating on R internal SEXP We provide a natural object mapping between R and C++ using a class framework We enable immediate prototyping using extensions added to inline We also facilitate easy package building Extensions offer e.g. efficient templated linear algebra Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • Fine for Indiana Jones
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples R support for C/C++ R is a C program R supports C++ out of the box, just use a .cpp file extension R exposes a API based on low level C functions and MACROS. R provides several calling conventions to invoke compiled code. SEXP foo( SEXP x1, SEXP x2 ){ ... } > .Call( "foo", 1:10, rnorm(10) ) Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples .Call example #include <R.h> #include <Rdefines.h> extern "C"SEXP vectorfoo(SEXP a, SEXP b){ int i, n; double *xa, *xb, *xab; SEXP ab; PROTECT(a = AS_NUMERIC(a)); PROTECT(b = AS_NUMERIC(b)); n = LENGTH(a); PROTECT(ab = NEW_NUMERIC(n)); xa=NUMERIC_POINTER(a); xb=NUMERIC_POINTER(b); xab = NUMERIC_POINTER(ab); double x = 0.0, y = 0.0 ; for (i=0; i<n; i++) xab[i] = 0.0; for (i=0; i<n; i++) { x = xa[i]; y = xb[i]; res[i] = (x < y) ? x*x : -(y*y); } UNPROTECT(3); return(ab); } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples .Call example: character vectors > c( "foo", "bar" ) #include <R.h> #include <Rdefines.h> extern "C"SEXP foobar(){ SEXP res = PROTECT(allocVector(STRSXP, 2)); SET_STRING_ELT( res, 0, mkChar( "foo") ) ; SET_STRING_ELT( res, 1, mkChar( "bar") ) ; UNPROTECT(1) ; return res ; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Why R? Context R API Overview Examples .Call example: calling an R function > eval( call( "rnorm", 3L, 10.0, 20.0 ) ) #include <R.h> #include <Rdefines.h> extern "C"SEXP callback(){ SEXP call = PROTECT( LCONS( install("rnorm"), CONS( ScalarInteger( 3 ), CONS( ScalarReal( 10.0 ), CONS( ScalarReal( 20.0 ), R_NilValue ) ) ) ) ); SEXP res = PROTECT(eval(call, R_GlobalEnv)) ; UNPROTECT(2) ; return res ; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • The Rcpp API
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API Encapsulation of R objects (SEXP) into C++ classes: NumericVector, IntegerVector, ..., Function, Environment, Language, ... Conversion from R to C++ : as Conversion from C++ to R : wrap Interoperability with the Standard Template Library (STL) Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : classes Rcpp class R typeof Integer(Vector|Matrix) integer vectors and matrices Numeric(Vector|Matrix) numeric ... Logical(Vector|Matrix) logical ... Character(Vector|Matrix) character ... Raw(Vector|Matrix) raw ... Complex(Vector|Matrix) complex ... List list (aka generic vectors) ... Expression(Vector|Matrix) expression ... Environment environment Function function XPtr externalptr Language language S4 S4 ... ... Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : numeric vectors Create a vector: SEXP x ; NumericVector y( x ) ; // from a SEXP // cloning (deep copy) NumericVector z = clone<NumericVector>( y ) ; // of a given size (all elements set to 0.0) NumericVector y( 10 ) ; // ... specifying the value NumericVector y( 10, 2.0 ) ; // ... with elements generated NumericVector y( 10, ::Rf_unif_rand ) ; // with given elements NumericVector y = NumericVector::create( 1.0, 2.0 ) ; Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : environments Environment::global_env() ; Environment::empty_env() ; Environment::base_env() ; Environment::base_namespace() ; Environment::Rcpp_namespace() ; Environment env( 2 ) ; Environment env( "package:Rcpp") ; Environment Rcpp = Environment::Rcpp_namespace() ; Environment env = Rcpp.parent() ; Environment env = Rcpp.new_child(true) ; Environment Rcpp=Environment::namespace_env( "Rcpp"); Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : Lists for input / output Actual code from the earthmovdist package on R-Forge RcppExport SEXP emdL1(SEXP H1, SEXP H2, SEXP parms) { try { Rcpp::NumericVector h1(H1); // double vector based on H1 Rcpp::NumericVector h2(H2); // double vector based on H2 Rcpp::List rparam(parms); // parameter from R based on parms bool verbose = Rcpp::as<bool>(rparam["verbose"]); [...] return Rcpp::NumericVector::create(Rcpp::Named("dist", d)); } catch(std::exception &ex) { forward_exception_to_r(ex); } catch(...) { ::Rf_error("c++ exception (unknown reason)"); } return R_NilValue; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : example SEXP foo( SEXP xs, SEXP ys ){ Rcpp::NumericVector xx(xs), yy(ys) ; int n = xx.size() ; Rcpp::NumericVector res( n ) ; double x = 0.0, y = 0.0 ; for (int i=0; i<n; i++) { x = xx[i]; y = yy[i]; res[i] = (x < y) ? x*x : -(y*y); } return res ; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : example using namespace Rcpp ; SEXP bar(){ std::vector<double> z(10) ; List res = List::create( _["foo"] = NumericVector::create(1,2), _["bar"] = 3, _["bla"] = "yada yada", _["blo"] = z ) ; res.attr("class") = "myclass"; return res ; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : example Inspired from a question on r-help Faster code for t(apply(x,1,cumsum)) [,1] [,2] [,3] [,1] [,2] [,3] [1,] 1 5 9 [1,] 1 6 15 [2,] 2 6 10 → [2,] 2 8 18 [3,] 3 7 11 [3,] 3 10 21 [4,] 4 8 12 [4,] 4 12 24 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : example Inspired from a question on r-help Two R versions: > # quite slow > f.R1 <- function( x ){ + t(apply(probs, 1, cumsum)) + } > # faster > f.R2 <- function( x ){ + y <- x + for( i in 2:ncol(x)){ + y[,i] <- y[,i-1] + x[,i] + } + y + } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : example Inspired from a question on r-help SEXP foo( SEXP x ){ NumericMatrix input( x ); // grab the number of rows and columns int nr = input.nrow(), nc = input.ncol(); // create a new matrix to store the results NumericMatrix output = clone<NumericMatrix>(input); // edit the current column of the output using the previous // column and the current input column for( int i=1; i<nc; i++) output.column(i) = output.column(i-1) + input.column(i); return output; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : example Inspired from a question on r-help version elapsed time relative f.Rcpp 0.25 1.00 f.R2 0.46 1.87 f.R1 12.05 49.16 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion Using STL algorithms C++ version of lapply using std::transform src <- ’ Rcpp::List input(data); Rcpp::Function f(fun); Rcpp::List output(input.size()); std::transform( input.begin(), input.end(), output.begin(), f ); output.names() = input.names(); return output; ’ cpp_lapply <- cxxfunction( signature(data="list", fun = "function"), src, plugin="Rcpp") Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion Simple C++ version of lapply Using the function > cpp_lapply( faithful, summary ) $eruptions Min. 1st Qu. Median Mean 3rd Qu. Max. 1.600 2.163 4.000 3.488 4.454 5.100 $waiting Min. 1st Qu. Median Mean 3rd Qu. Max. 43.0 58.0 76.0 70.9 82.0 96.0 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : example Calling an R function From a project we are currently working on: double evaluate(SEXP par_, SEXP fun_, SEXP rho_) { Rcpp::NumericVector par(par_); Rcpp::Function fun(fun_); Rcpp::Environment env(rho); Rcpp::Language funcall(fun, par); double res = Rcpp::as<double>(funcall.eval(env)); return(res); } Yet using Rcpp here repeatedly as in function optimization is not yet competitive. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : example Calling an R function (plain API variant) double evaluate(const double *param, SEXP par, SEXP fcall, SEXP env) { // -- faster: direct access _assuming_numeric vector memcpy(REAL(par), param, Rf_nrows(par) * sizeof(double)); SEXP fn = ::Rf_lang2(fcall, par); // could be done with Rcpp SEXP sexp_fvec = ::Rf_eval(fn, env);// but is slower right now double res = Rcpp::as<double>(sexp_fvec); return(res); } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : conversion from R to C++ Rcpp::as<T> handles conversion from SEXP to T. template <typename T> T as( SEXP m_sexp) throw(not_compatible) ; T can be: primitive type : int, double, bool, long, std::string any type that has a constructor taking a SEXP ... that specializes the as template ... that specializes the Exporter class template containers from the STL more details in the Rcpp-extending vignette. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : conversion from C++ to R Rcpp::wrap<T> handles conversion from T to SEXP. template <typename T> SEXP wrap( const T& object ) ; T can be: primitive type : int, double, bool, long, std::string any type that has a an operator SEXP ... that specializes the wrap template ... that has a nested type called iterator and member functions begin and end containers from the STL vector<T>, list<T>, map<string,T>, etc ... (where T is itself wrappable) more details in the Rcpp-extending vignette. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : conversion examples typedef std::vector<double> Vec ; int x_= as<int>( x ) ; double y_= as<double>( y_) ; VEC z_= as<VEC>( z_) ; wrap( 1 ) ; // INTSXP wrap( "foo") ; // STRSXP typedef std::map<std::string,Vec> Map ; Map foo( 10 ) ; Vec f1(4) ; Vec f2(10) ; foo.insert( "x", f1 ) ; foo.insert( "y", f2 ) ; wrap( foo ) ; // named list of numeric vectors Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview Conversion The Rcpp API : implicit conversion examples Environment env = ... ; List list = ... ; Function rnorm( "rnorm") ; // implicit calls to as int x = env["x"] ; double y = list["y"]; // implicit calls to wrap rnorm( 100, _["mean"] = 10 ) ; env["x"] = 3; env["y"] = "foo"; List::create( 1, "foo", 10.0, false ) ; Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • inline
  • R API inline Sugar R* Objects Modules End The inline package inline by Oleg Sklyar et al is a wonderfully useful little package. We extended it to work with Rcpp (and related packages such as RcppArmadillo, see below). # default plugin fx <- cxxfunction(signature(x = "integer", y = "numeric") , ’return ScalarReal( INTEGER(x)[0] * REAL(y)[0] ); ’) fx( 2L, 5 ) # Rcpp plugin fx <- cxxfunction(signature(x = "integer", y = "numeric"), ’return wrap(as<int>(x) * as<double>(y));’, plugin = "Rcpp") fx( 2L, 5 ) Compiles, links and loads C, C++ and Fortran. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End The inline package Also works for templated code – cf Whit on rcpp-devel last month inc <- ’ #include <iostream> #include <armadillo> #include <cppbugs/cppbugs.hpp> using namespace arma; using namespace cppbugs; class TestModel: public MCModel { public: const mat& y; // given const mat& X; // given Normal<vec> b; Uniform<double> tau_y; Deterministic<mat> y_hat; Normal<mat> likelihood; Deterministic<double> rsq; TestModel(const mat& y_,const mat& X_): y(y_), X(X_), b(randn<vec>(X_.n_cols)), tau_y(1), y_hat(X*b.value), likelihood(y_,true), rsq(0) { [...] ’ inc= includes headers before the body= — and the templated CppBUGS package by Whit now outperforms PyMC / Bugs. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • Rcpp sugar
  • R API inline Sugar R* Objects Modules End Motivation Example Contents Performance Sugar : motivation int n = x.size() ; NumericVector res1( n ) ; double x_= 0.0, y_= 0.0 ; for( int i=0; i<n; i++){ x_= x[i] ;y_= y[i] ; if( R_IsNA(x_) ||R_IsNA(y_) ){ res1[i] = NA_REAL; } else if( x_< y_){ res1[i] = x_* x_; } else { res1[i] = -( y_* y_) ; } } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Motivation Example Contents Performance Sugar : motivation We missed the R syntax : > ifelse( x < y, x*x, -(y*y) ) sugar brings it into C++ SEXP foo( SEXP xx, SEXP yy){ NumericVector x(xx), y(yy) ; return ifelse( x < y, x*x, -(y*y) ) ; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Motivation Example Contents Performance Sugar : another example double square( double x){ return x*x ; } SEXP foo( SEXP xx ){ NumericVector x(xx) ; return sapply( x, square ) ; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Motivation Example Contents Performance Sugar : contents logical operators: <, >, <=, >=, ==, != arithmetic operators: +, -, *, / functions on vectors: abs, all, any, ceiling, diag, diff, exp, head, ifelse, is_na, lapply, pmin, pmax, pow, rep, rep_each, rep_len, rev, sapply, seq_along, seq_len, sign, tail functions on matrices: outer, col, row, lower_tri, upper_tri, diag statistical functions (dpqr) : rnorm, dpois, qlogis, etc ... More information in the Rcpp-sugar vignette. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Motivation Example Contents Performance Sugar : benchmarks expression sugar R R / sugar any(x*y<0) 0.000447 4.86 10867 ifelse(x<y,x*x,-(y*y)) 1.331 22.29 16.74 ( ) ifelse(x<y,x*x,-(y*y)) 0.832 21.59 24.19 sapply(x,square) 0.240 138.71 577.39 Benchmarks performed on OSX SL / R 2.12.0 alpha (64 bit) on a MacBook Pro (i5). : version includes optimization related to the absence of missing values Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Motivation Example Contents Performance Sugar : benchmarks Benchmarks of the convolution example from Writing R Extensions. Implementation Time in Relative millisec to R API R API (as benchmark) 218 Rcpp sugar 145 0.67 NumericVector::iterator 217 1.00 NumericVector::operator[] 282 1.29 RcppVector<double> 683 3.13 Table: Convolution of x and y (200 values), repeated 5000 times. Extract from the article Rcpp: Seamless R and C++ integration, accepted for publication in the R Journal. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • RInside
  • R API inline Sugar R* Objects Modules End Background Examples Summary From RApache to littler to RInside See the file RInside/standard/rinside_sample0.cpp Jeff Horner’s work on RApache lead to joint work in littler, a scripting / cmdline front-end. As it embeds R and simply ’feeds’ the REPL loop, the next step was to embed R in proper C++ classes: RInside. #include <RInside.h> // for the embedded R via RInside int main(int argc, char *argv[]) { RInside R(argc, argv); // create an embedded R instance R["txt"] = "Hello, world!n"; // assign a char* (string) to ’txt’ R.parseEvalQ("cat(txt)"); // eval init string, ignore any returns exit(0); } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Background Examples Summary Another simple example See RInside/standard/rinside_sample8.cpp (in SVN, older version in pkg) This shows some of the assignment and converter code: #include <RInside.h> // for the embedded R via RInside int main(int argc, char *argv[]) { RInside R(argc, argv); // create an embedded R instance R["x"] = 10 ; R["y"] = 20 ; R.parseEvalQ("z <- x + y") ; int sum = R["z"]; std::cout << "10 + 20 = "<< sum << std::endl ; exit(0); } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Background Examples Summary A finance example See the file RInside/standard/rinside_sample4.cpp (edited) #include <RInside.h> // for the embedded R via RInside #include <iomanip> int main(int argc, char *argv[]) { RInside R(argc, argv); // create an embedded R instance SEXP ans; R.parseEvalQ("suppressMessages(library(fPortfolio))"); txt = "lppData <- 100 * LPP2005.RET[, 1:6]; " "ewSpec <- portfolioSpec(); nAssets <- ncol(lppData); "; R.parseEval(txt, ans); // prepare problem const double dvec[6] = { 0.1, 0.1, 0.1, 0.1, 0.3, 0.3 }; // weights const std::vector<double> w(dvec, &dvec[6]); R.assign( w, "weightsvec"); // assign STL vec to Rs weightsvec R.parseEvalQ("setWeights(ewSpec) <- weightsvec"); txt = "ewPortfolio <- feasiblePortfolio(data = lppData, spec = ewSpec, " " constraints = "LongOnly"); " "print(ewPortfolio); " "vec <- getCovRiskBudgets(ewPortfolio@portfolio)"; ans = R.parseEval(txt); // assign covRiskBudget weights to ans Rcpp::NumericVector V(ans); // convert SEXP variable to an RcppVector ans = R.parseEval("names(vec)"); // assign columns names to ans Rcpp::CharacterVector n(ans); for (int i=0; i<names.size(); i++) { std::cout << std::setw(16) << n[i] << "t"<< std::setw(11) << V[i] << "n"; } exit(0); } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Background Examples Summary RInside and C++ integration See the file RInside/standard/rinside_sample9.cpp #include <RInside.h> // for the embedded R via RInside // a c++ function we wish to expose to R const char* hello( std::string who ){ std::string result( "hello ") ; result += who ; return result.c_str() ; } int main(int argc, char *argv[]) { // create an embedded R instance RInside R(argc, argv); // expose the "hello" function in the global environment R["hello"] = Rcpp::InternalFunction( &hello ) ; // call it and display the result std::string result = R.parseEval("hello(’world’)") ; std::cout << "hello( ’world’) = "<< result << std::endl ; exit(0); } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Background Examples Summary And another parallel example See the file RInside/mpi/rinside_mpi_sample2.cpp // MPI C++ API version of file contributed by Jianping Hua #include <mpi.h> // mpi header #include <RInside.h> // for the embedded R via RInside int main(int argc, char *argv[]) { MPI::Init(argc, argv); // mpi initialization int myrank = MPI::COMM_WORLD.Get_rank(); // obtain current node rank int nodesize = MPI::COMM_WORLD.Get_size(); // obtain total nodes running. RInside R(argc, argv); // create an embedded R instance std::stringstream txt; txt << "Hello from node "<< myrank // node information << " of "<< nodesize << " nodes!"<< std::endl; R.assign( txt.str(), "txt"); // assign string to R variable txt std::string evalstr = "cat(txt)"; // show node information R.parseEvalQ(evalstr); // eval the string, ign. any returns MPI::Finalize(); // mpi finalization exit(0); } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Background Examples Summary RInside workflow C++ programs compute, gather or aggregate raw data. Data is saved and analysed before a new ’run’ is launched. With RInside we now skip a step: collect data in a vector or matrix pass data to R — easy thanks to Rcpp wrappers pass one or more short ’scripts’ as strings to R to evaluate pass data back to C++ programm — easy thanks to Rcpp converters resume main execution based on new results A number of simple examples ship with RInside nine different examples in examples/standard four different examples in examples/mpi Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • RProtoBuf
  • R API inline Sugar R* Objects Modules End About Google ProtoBuf Quoting from the page at Google Code: Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data—think XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. Google provides native bindings for C++, Java and Python. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Example from the protobuf page Create/update a message message Person { required int32 id = 1; required string name = 2; optional string email = 3; } C++ R/RProtoBuf Person person; > library( RProtoBuf ) person.set_id(123); > ## create Bob person.set_name("Bob"); > bob <- new( tutorial.Person) person.set_email("bob@example.com"); > ## assign to components > bob$id <- 123 fstream out("person.pb", ios::out > bob$name <- "Bob" |ios::binary |ios::trunc); > bob$email <- "bob@example.com" person.SerializeToOstream(&out); > ## and write out out.close(); > serialize( bob, "person.pb" ) Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Example from the protobuf page Reading from a file and access content of the message C++ Person person; fstream in("person.pb", ios::in |ios::binary); R/RProtoBuf if (!person.ParseFromIstream(&in)) { cerr << "Failed to parse person.pb."<< > person <- read(tutorial.Person, "person.pb") endl; > cat( "ID: ", person$id, "n" ) exit(1); > cat( "name: ", person$name, "n" ) } > if( person$has( "email" ) ){ + cat( "email: ", person$email, "n" ) cout << "ID: "<< person.id() << endl; + } cout << "name: "<< person.name() << endl; if (person.has_email()) { cout << "e-mail: "<< person.email() << endl; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • RcppArmadillo
  • R API inline Sugar R* Objects Modules End Linear regression via Armadillo: lmArmadillo example Also see the directory Rcpp/examples/FastLM lmArmadillo <- function() { src <- ’ Rcpp::NumericVector yr(Ysexp); Rcpp::NumericVector Xr(Xsexp); // actually an n x k matrix std::vector<int> dims = Xr.attr("dim"); int n = dims[0], k = dims[1]; arma::mat X(Xr.begin(), n, k, false); // use advanced armadillo constructors arma::colvec y(yr.begin(), yr.size()); arma::colvec coef = solve(X, y); // model fit arma::colvec resid = y - X*coef; // comp. std.errr of the coefficients arma::mat covmat = trans(resid)*resid/(n-k) * arma::inv(arma::trans(X)*X); Rcpp::NumericVector coefr(k), stderrestr(k); for (int i=0; i<k; i++) { // with RcppArmadillo templ. conv. coefr[i] = coef[i]; // this would not be needed but we only stderrestr[i] = sqrt(covmat(i,i)); // assume Rcpp.h here } return Rcpp::List::create(Rcpp::Named( "coefficients", coefr), Rcpp::Named( "stderr", stderrestr)); ’ ## turn into a function that R can call fun <- cppfunction(signature(Ysexp="numeric", Xsexp="numeric"), src, plugin="RcppArmadillo") } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Linear regression via Armadillo: RcppArmadillo See fastLm in the RcppArmadillo package fastLm in the new RcppArmadillo release does even better: #include <RcppArmadillo.h> extern "C"SEXP fastLm(SEXP ys, SEXP Xs) { try { Rcpp::NumericVector yr(ys); // creates Rcpp vector from SEXP Rcpp::NumericMatrix Xr(Xs); // creates Rcpp matrix from SEXP int n = Xr.nrow(), k = Xr.ncol(); arma::mat X(Xr.begin(), n, k, false); // reuses memory and avoids extra copy arma::colvec y(yr.begin(), yr.size(), false); arma::colvec coef = arma::solve(X, y); // fit model y ∼ X arma::colvec res = y - X*coef; // residuals double s2 = std::inner_product(res.begin(),res.end(),res.begin(),double())/(n-k); // std.errors of coefficients arma::colvec stderr = arma::sqrt(s2*arma::diagvec(arma::inv(arma::trans(X)*X))); return Rcpp::List::create(Rcpp::Named("coefficients") = coef, Rcpp::Named("stderr") = stderr, Rcpp::Named("df") = n - k); } catch( std::exception &ex ) { forward_exception_to_r( ex ); } catch(...) { ::Rf_error( "c++ exception (unknown reason)"); } return R_NilValue; // -Wall } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Linear regression via GNU GSL: RcppGSL See fastLm in the RcppGSL package (on R-Forge) #include <RcppArmadillo.h> extern "C"SEXP fastLm(SEXP ys, SEXP Xs) { BEGIN_RCPP RcppGSL::vector<double> y = ys; // create gsl data structures from SEXP RcppGSL::matrix<double> X = Xs; int n = X.nrow(), k = X.ncol(); double chisq; RcppGSL::vector<double> coef(k); // to hold the coefficient vector RcppGSL::matrix<double> cov(k,k); // and the covariance matrix // the actual fit requires working memory we allocate and free gsl_multifit_linear_workspace *work = gsl_multifit_linear_alloc (n, k); gsl_multifit_linear (X, y, coef, cov, &chisq, work); gsl_multifit_linear_free (work); // extract the diagonal as a vector view gsl_vector_view diag = gsl_matrix_diagonal(cov) ; // currently there is not a more direct interface in Rcpp::NumericVector // that takes advantage of wrap, so we have to do it in two steps Rcpp::NumericVector stderr ; stderr = diag; std::transform( stderr.begin(), stderr.end(), stderr.begin(), sqrt ); Rcpp::List res = Rcpp::List::create(Rcpp::Named("coefficients") = coef, Rcpp::Named("stderr") = stderr, Rcpp::Named("df") = n - k); // free all the GSL vectors and matrices -- as these are really C data structures // we cannot take advantage of automatic memory management coef.free(); cov.free(); y.free(); X.free(); return res; // return the result list to R END_RCPP } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • Objects
  • Lexical Scoping S3 classes S4 classes Reference (R5) classes C++ classes Protocol Buffers
  • Fil rouge: bank account example Data: - The balance - Authorized overdraft Operations: - Open an account - Get the balance - Deposit - Withdraw .
  • R API inline Sugar R* Objects Modules End Lexcial Scoping > open.account <- function(total, overdraft = 0.0){ + deposit <- function(amount) { + if( amount < 0 ) + stop( "deposits must be positive" ) + total <<- total + amount + } + withdraw <- function(amount) { + if( amount < 0 ) + stop( "withdrawals must be positive" ) + if( total - amount < overdraft ) + stop( "you cannot withdraw that much" ) + total <<- total - amount + } + balance <- function() { + total + } + list( deposit = deposit, withdraw = withdraw, + balance = balance ) + } > romain <- open.account(500) > romain$balance() [1] 500 > romain$deposit(100) > romain$withdraw(200) > romain$balance() [1] 400 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End S3 classes Any R object with a class attribute Very easy Very dangerous Behaviour is added through S3 generic functions > Account <- function( total, overdraft = 0.0 ){ + out <- list( balance = total, overdraft = overdraft ) + class( out ) <- "Account" + out + } > balance <- function(x){ + UseMethod( "balance" ) + } > balance.Account <- function(x) x$balance Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End S3 classes > deposit <- function(x, amount){ + UseMethod( "deposit" ) + } > deposit.Account <- function(x, amount) { + if( amount < 0 ) + stop( "deposits must be positive" ) + x$balance <- x$balance + amount + x + } > withdraw <- function(x, amount){ + UseMethod( "withdraw" ) + } > withdraw.Account <- function(x, amount) { + if( amount < 0 ) + stop( "withdrawals must be positive" ) + if( x$balance - amount < x$overdraft ) + stop( "you cannot withdraw that much" ) + x$balance <- x$balance - amount + x + } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End S3 classes Example use: > romain <- Account( 500 ) > balance( romain ) [1] 500 > romain <- deposit( romain, 100 ) > romain <- withdraw( romain, 200 ) > balance( romain ) [1] 400 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End S4 classes Formal class definition Validity checking Formal generic functions and methods Very verbose, both in code and documentation Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End S4 classes > setClass( "Account", + representation( + balance = "numeric", + overdraft = "numeric" + ), + prototype = prototype( + balance = 0.0, + overdraft = 0.0 + ), + validity = function(object){ + object@balance > object@overdraft + } + ) [1] "Account" > setGeneric( "balance", + function(x) standardGeneric( "balance" ) + ) [1] "balance" > setMethod( "balance", "Account", + function(x) x@balance + ) [1] "balance" Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End S4 classes > setGeneric( "deposit", + function(x, amount) standardGeneric( "deposit" ) + ) [1] "deposit" > setMethod( "deposit", + signature( x = "Account", amount = "numeric" ), + function(x, amount){ + new( "Account" , + balance = x@balance + amount, + overdraft = x@overdraft + ) + } + ) [1] "deposit" Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End S4 classes > romain <- new( "Account", balance = 500 ) > balance( romain ) [1] 500 > romain <- deposit( romain, 100 ) > romain <- withdraw( romain, 200 ) > balance( romain ) [1] 400 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Reference (R5) classes Real S4 classes: formalism, dispatch, ... Passed by Reference Easy to use Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Reference (R5) classes > Account <- setRefClass( "Account_R5", + fields = list( + balance = "numeric", + overdraft = "numeric" + ), + methods = list( + withdraw = function( amount ){ + if( amount < 0 ) + stop( "withdrawal must be positive" ) + if( balance - amount < overdraft ) + stop( "overdrawn" ) + balance <<- balance - amount + }, + deposit = function(amount){ + if( amount < 0 ) + stop( "deposits must be positive" ) + balance <<- balance + amount + } + ) + ) > x <- Account$new( balance = 10.0, overdraft = 0.0 ) > x$withdraw( 5 ) > x$deposit( 10 ) > x$balance [1] 15 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Reference (R5) classes Real pass by reference : > borrow <- function( x, y, amount = 0.0 ){ + x$withdraw( amount ) + y$deposit( amount ) + invisible(NULL) + } > romain <- Account$new( balance = 5000, overdraft = 0.0 ) > dirk <- Account$new( balance = 3, overdraft = 0.0 ) > borrow( romain, dirk, 2000 ) > romain$balance [1] 3000 > dirk$balance [1] 2003 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Reference (R5) classes Adding a method dynamically to a class : > Account$methods( + borrow = function(other, amount){ + deposit( amount ) + other$withdraw( amount ) + invisible(NULL) + } + ) > romain <- Account$new( balance = 5000, overdraft = 0.0 ) > dirk <- Account$new( balance = 3, overdraft = 0.0 ) > dirk$borrow( romain, 2000 ) > romain$balance [1] 3000 > dirk$balance [1] 2003 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End C++ classes class Account { public: Account() : balance(0.0), overdraft(0.0){} void withdraw( double amount ){ if( balance - amount < overdraft ) throw std::range_error( "no way") ; balance -= amount ; } void deposit( double amount ){ balance += amount ; } double balance ; private: double overdraft ; } ; Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End C++ classes Exposing to R through Rcpp modules: RCPP_MODULE(yada){ class_<Account>( "Account") // expose the field .field_readonly( "balance", &Account::balance ) // expose the methods .method( "withdraw", &Account::withdraw ) .method( "deposit", &Account::deposit ) ; } Use it in R: > Account <- yada$Account > romain <- Account$new() > romain$deposit( 10 ) > romain$withdraw( 2 ) > romain$balance [1] 8 Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Protocol Buffers Define the message type, in Account.proto : package foo ; message Account { required double balance = 1 ; required double overdraft = 2 ; } Load it into R with RProtoBuf: > require( RProtoBuf ) > loadProtoFile( "Account.proto" ) Use it: > romain <- new( foo.Account, + balance = 500, overdraft = 10 ) > romain$balance Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • Rcpp modules
  • R API inline Sugar R* Objects Modules End Overview R side Modules: expose C++ to R const char* hello( const std::string& who ){ std::string result( "hello ") ; result += who ; return result.c_str() ; } RCPP_MODULE(yada){ using namespace Rcpp ; function( "hello", &hello ) ; } > yada <- Module( "yada" ) > yada$hello( "world" ) Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview R side Modules: expose C++ classes to R class World { public: World() : msg("hello"){} void set(std::string msg) { this->msg = msg; } std::string greet() { return msg; } private: std::string msg; }; void clearWorld( World* w){ w->set( "") ; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview R side Modules: expose C++ classes to R C++ side: declare what to expose RCPP_MODULE(yada){ using namespace Rcpp ; class_<World>( "World") .method( "greet", &World::greet ) .method( "set", &World::set ) .method( "clear", &clearWorld ) ; } Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Overview R side Modules: on the R side R side: based on R 2.12.0 reference classes (aka R5), see ?ReferenceClasses > World <- yada$World > w <- new( World ) > w$greet() [1] "hello" > w$set( "hello world") > w$greet() [1] "hello world" > w$clear() > w$greet() [1] "" Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Creating a package using Rcpp A simple yet reliable strategy is to prototype code using inline call package.skeleton the resulting function generated by cxxfunction — and magic ensues Kidding aside, inline provides a variant of package.skeleton that knows how to employ the information in the generated function. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Creating a package using Rcpp foo <- cxxfunction(list(tic=signature(x="numeric",y="numeric"), tac=signature(x="numeric",y="numeric")), list(tic="return Rcpp::wrap( sqrt(pow(Rcpp::as<double>(x), 2) + pow(Rcpp::as<double>(y), 2)));", tac="return Rcpp::wrap( sqrt(fabs(Rcpp::as<double>(x)) + fabs(Rcpp::as<double>(y))));"), plugin="Rcpp") foo$tic(-2, 3) foo$tac( 2, -3) package.skeleton("myPackage", foo) Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Further Reading Rcpp comes with eight vignettes: Rcpp-introduction: A overview article covering the core features Rcpp-FAQ: Answers to (in)frequently asked questions Rcpp-package: How to use Rcpp in your own package Rcpp-extensions: How to extend Rcpp as RcppArmadillo or RcppGSL do Rcpp-sugar: An overview of ’Rcpp sugar’ Rcpp-modules: An overview of ’Rcpp modules Rcpp-quickref: A quick reference guide to the Rcpp API Rcpp-unittest: Autogenerated results from running 700+ unit tests Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • R API inline Sugar R* Objects Modules End Further Reading The unit tests also provide usage examples. CRAN now lists fifteen packages depending on Rcpp – these also provide working examples. The rcpp-devel mailing list (and its archive) is a further resource. Dirk Eddelbuettel and Romain François Integrating R with C++: Rcpp, RInside and RProtoBuf
  • Want to learn more ? Check the vignettes Questions on the Rcpp-devel mailing list Hands-on training courses Commercial support Romain François romain@r-enthusiasts.com Dirk Eddelbuettel edd@debian.org